A novel multiple kernel learning approach for semi-supervised clustering
| Autor: | Mohammad Taghi Sadeghi, Hamid Reza Abutalebi, T. Zare |
|---|---|
| Rok vydání: | 2013 |
| Předmět: |
Computer Science::Machine Learning
Multiple kernel learning business.industry Pattern recognition Semi-supervised learning Machine learning computer.software_genre Kernel method Kernel embedding of distributions Polynomial kernel Metric (mathematics) Radial basis function kernel Artificial intelligence Tree kernel business computer Mathematics |
| Zdroj: | 2013 8th Iranian Conference on Machine Vision and Image Processing (MVIP). |
| DOI: | 10.1109/iranianmvip.2013.6780028 |
| Popis: | Distance metrics are widely used in various machine learning and pattern recognition algorithms. A main issue in these algorithms is choosing the proper distance metric. In recent years, learning an appropriate distance metric has become a very active research field. In the kernelised version of distance metric learning algorithms, the data points are implicitly mapped into a higher dimensional feature space and the learning process is performed in the resulted feature space. The performance of the kernel-based methods heavily depends on the chosen kernel function. So, selecting an appropriate kernel function and/or tuning its parameter(s) impose significant challenges in such methods. The Multiple Kernel Learning theory (MKL) addresses this problem by learning a linear combination of a number of predefined kernels. In this paper, we formulate the MKL problem in a semi-supervised metric learning framework. In the proposed approach, pairwise similarity constraints are used to adjust the weights of the combined kernels and simultaneously learn the appropriate distance metric. Using both synthetic and real-world datasets, we show that the proposed method outperforms some recently introduced semi-supervised metric learning approaches. |
| Databáze: | OpenAIRE |
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